{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This workshop's goal&mdash;which is facilitated by this Jupyter notebook&mdash;is to give attendees the confidence to use `pandas` in their research projects. Basic familiarity with Python *is* assumed.\n",
    "\n",
    "`pandas` is designed to make it easier to work with structured data. Most of the analyses you might perform will likely involve using tabular data, e.g., from .csv files or relational databases (e.g., SQL). The `DataFrame` object in `pandas` is \"a two-dimensional tabular, column-oriented data structure with both row and column labels.\"\n",
    "\n",
    "If you're curious:\n",
    "\n",
    ">The `pandas` name itself is derived from *panel data*, an econometrics term for multidimensional structured data sets, and *Python data analysis* itself.\n",
    "\n",
    "To motivate this workshop, we'll work with example data and go through the various steps you might need to prepare data for analysis. You'll (hopefully) realize that doing this type of work is much more difficult using Python's built-in data structures.\n",
    "\n",
    "The data used in these examples is available in the following [GitHub repository](https://github.com/juanshishido/introduction-to-pandas). If you've [cloned that repo](https://www.atlassian.com/git/tutorials/setting-up-a-repository/git-clone), which is the recommended approach, you'll have everything you need to run this notebook. Otherwise, you can download the data file(s) from the above link. (Note: this notebook assumes that the data files are in a directory named `data/` found within your current working directory.)\n",
    "\n",
    "For this example, we're working with European unemployment data from Eurostat, which is hosted by [Google](https://code.google.com/p/dspl/downloads/list). There are several .csv files that we'll work with in this workshop.\n",
    "\n",
    "Let's begin by importing `pandas` using the conventional abbreviation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "mpl.rc('savefig', dpi=200)\n",
    "plt.style.use('ggplot')\n",
    "plt.rcParams['xtick.minor.size'] = 0\n",
    "plt.rcParams['ytick.minor.size'] = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `read_csv()` function in `pandas` allows us to easily import our data. By default, it assumes the data is comma-delimited. However, you can specify the delimiter used in your data (e.g., tab, semicolon, pipe, etc.). There are several parameters that you can specify. See the documentation [here](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html). `read_csv()` returns a `DataFrame`.\n",
    "\n",
    "Notice that we call `read_csv()` using the `pd` abbreviation from the import statement above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "unemployment = pd.read_csv('data/country_total.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great! You've created a `pandas` `DataFrame`. We can look at our data by using the `.head()` method. By default, this shows the header (column names) and the first five rows. Passing an integer, $n$, to `.head()` returns that number of rows. To see the last $n$ rows, use `.tail()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>seasonality</th>\n",
       "      <th>month</th>\n",
       "      <th>unemployment</th>\n",
       "      <th>unemployment_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>at</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1993.01</td>\n",
       "      <td>171000</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>at</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1993.02</td>\n",
       "      <td>175000</td>\n",
       "      <td>4.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>at</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1993.03</td>\n",
       "      <td>166000</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>at</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1993.04</td>\n",
       "      <td>157000</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>at</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1993.05</td>\n",
       "      <td>147000</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country seasonality    month  unemployment  unemployment_rate\n",
       "0      at         nsa  1993.01        171000                4.5\n",
       "1      at         nsa  1993.02        175000                4.6\n",
       "2      at         nsa  1993.03        166000                4.4\n",
       "3      at         nsa  1993.04        157000                4.1\n",
       "4      at         nsa  1993.05        147000                3.9"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unemployment.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To find the number of rows, you can use the `len()` function. Alternatively, you can use the `shape` attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20796, 5)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unemployment.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 20,796 rows and 5 columns.\n",
    "\n",
    "You may have noticed that the `month` column also includes the year. Let's go ahead and rename it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "unemployment.rename(columns={'month' : 'year_month'}, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `.rename()` method allows you to modify index labels and/or column names. As you can see, we passed a `dict` to the `columns` parameter, with the original name as the key and the new name as the value. Importantly, we also set the `inplace` parameter to `True`, which modifies the *actual* `DataFrame`, not a copy of it.\n",
    "\n",
    "It might also make sense to separate the data in `year_month` into two separate columns. To do this, you'll need to know how to select a single column. We can either use bracket (`[]`) or dot notation (referred to as *attribute access*)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1993.01\n",
       "1    1993.02\n",
       "2    1993.03\n",
       "3    1993.04\n",
       "4    1993.05\n",
       "Name: year_month, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unemployment['year_month'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1993.01\n",
       "1    1993.02\n",
       "2    1993.03\n",
       "3    1993.04\n",
       "4    1993.05\n",
       "Name: year_month, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unemployment.year_month.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is preferrable to use the bracket notation as a column name might inadvertently have the same name as a `DataFrame` (or `Series`) method. In addition, only bracket notation can be used to create a new column. If you try and use attribute access to create a new column, you'll create a new attribute, *not* a new column.\n",
    "\n",
    "When selecting a single column, we have a `pandas` `Series` object, which is a single vector of data (e.g., a NumPy array) with \"an associated array of data labels, called its *index*.\" A `DataFrame` also has an index. In our example, the indices are an array of sequential integers, which is the default. You can find them in the left-most position, without a column label.\n",
    "\n",
    "Indices need not be a sequence of integers. They can, for example, be dates or strings. Note that indices do *not* need to be unique.\n",
    "\n",
    "Indices, like column names, can be used to select data. Indices can be used to select particular rows. In fact, you can do something like `.head()` with slicing using the `[]` operator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>seasonality</th>\n",
       "      <th>year_month</th>\n",
       "      <th>unemployment</th>\n",
       "      <th>unemployment_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>at</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1993.01</td>\n",
       "      <td>171000</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>at</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1993.02</td>\n",
       "      <td>175000</td>\n",
       "      <td>4.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>at</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1993.03</td>\n",
       "      <td>166000</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>at</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1993.04</td>\n",
       "      <td>157000</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>at</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1993.05</td>\n",
       "      <td>147000</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country seasonality  year_month  unemployment  unemployment_rate\n",
       "0      at         nsa     1993.01        171000                4.5\n",
       "1      at         nsa     1993.02        175000                4.6\n",
       "2      at         nsa     1993.03        166000                4.4\n",
       "3      at         nsa     1993.04        157000                4.1\n",
       "4      at         nsa     1993.05        147000                3.9"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unemployment[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### More on Indexing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we continue, let's look at a few useful ways to index data&mdash;that is, select rows.\n",
    "\n",
    "`.loc` primarily works with string labels. It accepts a single label, a list (or array) of labels, or a slice of labels (e.g., `'a' : 'f'`).\n",
    "\n",
    "Let's create a `DataFrame` to see how this works. (This is based on an [example](https://github.com/fonnesbeck/scipy2015_tutorial/blob/master/notebooks/1.%20Data%20Preparation.ipynb) from Chris Fonnesbeck's [Computational Statistics II Tutorial](https://github.com/fonnesbeck/scipy2015_tutorial).)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "bacteria = pd.DataFrame({'bacteria_counts' : [632, 1638, 569, 115],\n",
    "                         'other_feature' : [438, 833, 234, 298]},\n",
    "                         index=['Firmicutes', 'Proteobacteria', 'Actinobacteria', 'Bacteroidetes'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that we pass in a `dict`, where the keys correspond to column names and the values to the data. In this example, we've also set the indices&mdash;strings in this case&mdash;to be the taxon of each bacterium."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bacteria_counts</th>\n",
       "      <th>other_feature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Firmicutes</th>\n",
       "      <td>632</td>\n",
       "      <td>438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Proteobacteria</th>\n",
       "      <td>1638</td>\n",
       "      <td>833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actinobacteria</th>\n",
       "      <td>569</td>\n",
       "      <td>234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bacteroidetes</th>\n",
       "      <td>115</td>\n",
       "      <td>298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                bacteria_counts  other_feature\n",
       "Firmicutes                  632            438\n",
       "Proteobacteria             1638            833\n",
       "Actinobacteria              569            234\n",
       "Bacteroidetes               115            298"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bacteria"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, if we're interested in the values (row) associated with \"Actinobacteria,\" we can use `.loc` and the index name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bacteria_counts    569\n",
       "other_feature      234\n",
       "Name: Actinobacteria, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bacteria.loc['Actinobacteria']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This returns the column values for the specified row. Interestingly, we could have also used \"positional indexing,\" even though the indices are strings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bacteria_counts</th>\n",
       "      <th>other_feature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Actinobacteria</th>\n",
       "      <td>569</td>\n",
       "      <td>234</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                bacteria_counts  other_feature\n",
       "Actinobacteria              569            234"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bacteria[2:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The difference is that the former returns a `Series`, while the latter returns a `DataFrame`.\n",
    "\n",
    "Let's return to our unemployment data. Another indexing option, `.iloc`, primarily works with integer positions. To select specific rows, we can do the following."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>seasonality</th>\n",
       "      <th>year_month</th>\n",
       "      <th>unemployment</th>\n",
       "      <th>unemployment_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>at</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1993.02</td>\n",
       "      <td>175000</td>\n",
       "      <td>4.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>at</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1993.06</td>\n",
       "      <td>134000</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>at</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1993.07</td>\n",
       "      <td>128000</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>at</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1993.10</td>\n",
       "      <td>141000</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country seasonality  year_month  unemployment  unemployment_rate\n",
       "1      at         nsa     1993.02        175000                4.6\n",
       "5      at         nsa     1993.06        134000                3.5\n",
       "6      at         nsa     1993.07        128000                3.4\n",
       "9      at         nsa     1993.10        141000                3.7"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unemployment.iloc[[1, 5, 6, 9]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can select a range of rows and specify the step value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>seasonality</th>\n",
       "      <th>year_month</th>\n",
       "      <th>unemployment</th>\n",
       "      <th>unemployment_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>at</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1995.02</td>\n",
       "      <td>174000</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>at</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1995.07</td>\n",
       "      <td>123000</td>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>at</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1995.12</td>\n",
       "      <td>175000</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>at</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1996.05</td>\n",
       "      <td>159000</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>at</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1996.10</td>\n",
       "      <td>146000</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country seasonality  year_month  unemployment  unemployment_rate\n",
       "25      at         nsa     1995.02        174000                4.5\n",
       "30      at         nsa     1995.07        123000                3.3\n",
       "35      at         nsa     1995.12        175000                4.7\n",
       "40      at         nsa     1996.05        159000                4.3\n",
       "45      at         nsa     1996.10        146000                3.9"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unemployment.iloc[25:50:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(Note: As is typical in Python, the end position is not included. Therefore, we don't see the row associated with the index 50.)\n",
    "\n",
    "Indexing is important. You'll use it a lot. Below, we'll show how to index based on data values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, we still want to split `year_month` into two separate columns. Above, we saw that this column is type (technically, `dtype`) `float64`. We'll first extract the year using the `.astype()` method. This allows for type casting&mdash;basically converting from one type to another. We'll then subtract this value from `year_month`&mdash;to get the decimal portion of the value&mdash;and multiply the result by 100 and convert to `int`.\n",
    "\n",
    "For more information on `pandas` `dtype`s, check the documentation [here](http://pandas.pydata.org/pandas-docs/stable/basics.html#dtypes)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "unemployment['year'] = unemployment['year_month'].astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, we're casting the floating point values to integers. In Python, this [truncates the decimals](https://docs.python.org/2/library/stdtypes.html#numeric-types-int-float-long-complex).\n",
    "\n",
    "If you didn't know this, you could have used NumPy's `floor()` function, as follows.\n",
    "\n",
    "```\n",
    "import numpy as np\n",
    "\n",
    "unemployment['year'] = (np.floor(unemployment['year_month'])).astype(int)\n",
    "```\n",
    "\n",
    "Additionally, if you wanted to check whether the two approaches shown above result in the same set of values, you could something like the following.\n",
    "\n",
    "```\n",
    "(unemployment['year_month'].astype(int) == (np.floor(unemployment['year_month'])).astype(int)).all()\n",
    "```\n",
    "\n",
    "What this does is an element-wise comparison of the values in the corresponding arrays. The `.all()` method checks whether *all* elements are `True`.\n",
    "\n",
    "Finally, let's create our month variable as described above. (Because of the truncating that occurs when casting to `int`, we first round the values to the nearest whole number.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "unemployment['month'] = ((unemployment['year_month'] - unemployment['year']) * 100).round(0).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>seasonality</th>\n",
       "      <th>year_month</th>\n",
       "      <th>unemployment</th>\n",
       "      <th>unemployment_rate</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>at</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1993.01</td>\n",
       "      <td>171000</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1993</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>at</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1993.02</td>\n",
       "      <td>175000</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1993</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>at</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1993.03</td>\n",
       "      <td>166000</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1993</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>at</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1993.04</td>\n",
       "      <td>157000</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1993</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>at</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1993.05</td>\n",
       "      <td>147000</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1993</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>at</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1993.06</td>\n",
       "      <td>134000</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1993</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>at</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1993.07</td>\n",
       "      <td>128000</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1993</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>at</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1993.08</td>\n",
       "      <td>130000</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1993</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>at</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1993.09</td>\n",
       "      <td>132000</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1993</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>at</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1993.10</td>\n",
       "      <td>141000</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1993</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>at</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1993.11</td>\n",
       "      <td>156000</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1993</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>at</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1993.12</td>\n",
       "      <td>169000</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1993</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country seasonality  year_month  unemployment  unemployment_rate  year  \\\n",
       "0       at         nsa     1993.01        171000                4.5  1993   \n",
       "1       at         nsa     1993.02        175000                4.6  1993   \n",
       "2       at         nsa     1993.03        166000                4.4  1993   \n",
       "3       at         nsa     1993.04        157000                4.1  1993   \n",
       "4       at         nsa     1993.05        147000                3.9  1993   \n",
       "5       at         nsa     1993.06        134000                3.5  1993   \n",
       "6       at         nsa     1993.07        128000                3.4  1993   \n",
       "7       at         nsa     1993.08        130000                3.4  1993   \n",
       "8       at         nsa     1993.09        132000                3.5  1993   \n",
       "9       at         nsa     1993.10        141000                3.7  1993   \n",
       "10      at         nsa     1993.11        156000                4.1  1993   \n",
       "11      at         nsa     1993.12        169000                4.4  1993   \n",
       "\n",
       "    month  \n",
       "0       1  \n",
       "1       2  \n",
       "2       3  \n",
       "3       4  \n",
       "4       5  \n",
       "5       6  \n",
       "6       7  \n",
       "7       8  \n",
       "8       9  \n",
       "9      10  \n",
       "10     11  \n",
       "11     12  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unemployment.head(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unemployment['month'][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "To create the `month` column, we subtracted two vectors. This resulted in the decimal value in `year_month`. To transform the values to integers, we multiplied by 100.\n",
    "\n",
    "Now, let's say we wanted to reorder the columns in the `DataFrame`. For this, we use bracket notation again, passing in a list of column names in the order we'd like to see them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "unemployment = unemployment[['country', 'seasonality',\n",
    "                             'year_month', 'year', 'month',\n",
    "                             'unemployment', 'unemployment_rate']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>seasonality</th>\n",
       "      <th>year_month</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>unemployment</th>\n",
       "      <th>unemployment_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>at</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1993.01</td>\n",
       "      <td>1993</td>\n",
       "      <td>1</td>\n",
       "      <td>171000</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>at</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1993.02</td>\n",
       "      <td>1993</td>\n",
       "      <td>2</td>\n",
       "      <td>175000</td>\n",
       "      <td>4.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>at</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1993.03</td>\n",
       "      <td>1993</td>\n",
       "      <td>3</td>\n",
       "      <td>166000</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>at</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1993.04</td>\n",
       "      <td>1993</td>\n",
       "      <td>4</td>\n",
       "      <td>157000</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>at</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1993.05</td>\n",
       "      <td>1993</td>\n",
       "      <td>5</td>\n",
       "      <td>147000</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>at</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1993.06</td>\n",
       "      <td>1993</td>\n",
       "      <td>6</td>\n",
       "      <td>134000</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>at</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1993.07</td>\n",
       "      <td>1993</td>\n",
       "      <td>7</td>\n",
       "      <td>128000</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>at</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1993.08</td>\n",
       "      <td>1993</td>\n",
       "      <td>8</td>\n",
       "      <td>130000</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>at</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1993.09</td>\n",
       "      <td>1993</td>\n",
       "      <td>9</td>\n",
       "      <td>132000</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>at</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1993.10</td>\n",
       "      <td>1993</td>\n",
       "      <td>10</td>\n",
       "      <td>141000</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country seasonality  year_month  year  month  unemployment  \\\n",
       "0      at         nsa     1993.01  1993      1        171000   \n",
       "1      at         nsa     1993.02  1993      2        175000   \n",
       "2      at         nsa     1993.03  1993      3        166000   \n",
       "3      at         nsa     1993.04  1993      4        157000   \n",
       "4      at         nsa     1993.05  1993      5        147000   \n",
       "5      at         nsa     1993.06  1993      6        134000   \n",
       "6      at         nsa     1993.07  1993      7        128000   \n",
       "7      at         nsa     1993.08  1993      8        130000   \n",
       "8      at         nsa     1993.09  1993      9        132000   \n",
       "9      at         nsa     1993.10  1993     10        141000   \n",
       "\n",
       "   unemployment_rate  \n",
       "0                4.5  \n",
       "1                4.6  \n",
       "2                4.4  \n",
       "3                4.1  \n",
       "4                3.9  \n",
       "5                3.5  \n",
       "6                3.4  \n",
       "7                3.4  \n",
       "8                3.5  \n",
       "9                3.7  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unemployment.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "So far, our `DataFrame` is organized in a reasonable way. But, we know we can do better. We're eventually going to be interested in the unemployment rate for each country. The trouble is, we don't exactly know what the values in `country` refer to. We can fix that by getting country names. These can be found in `countries.csv`. However, instead of loading the file in `data/`, why not try something else?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "countries_url = 'https://raw.githubusercontent.com/dlab-berkeley/introduction-to-pandas/master/data/countries.csv'\n",
    "countries = pd.read_csv(countries_url)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`read_csv()` can take a URL for the file path."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>google_country_code</th>\n",
       "      <th>country_group</th>\n",
       "      <th>name_en</th>\n",
       "      <th>name_fr</th>\n",
       "      <th>name_de</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>se</td>\n",
       "      <td>SE</td>\n",
       "      <td>eu</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>Suède</td>\n",
       "      <td>Schweden</td>\n",
       "      <td>62.198468</td>\n",
       "      <td>14.896307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>tr</td>\n",
       "      <td>TR</td>\n",
       "      <td>non-eu</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>Turquie</td>\n",
       "      <td>Türkei</td>\n",
       "      <td>38.952942</td>\n",
       "      <td>35.439795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>uk</td>\n",
       "      <td>GB</td>\n",
       "      <td>eu</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Royaume-Uni</td>\n",
       "      <td>Vereinigtes Königreich</td>\n",
       "      <td>54.315447</td>\n",
       "      <td>-2.232612</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country google_country_code country_group         name_en      name_fr  \\\n",
       "27      se                  SE            eu          Sweden        Suède   \n",
       "28      tr                  TR        non-eu          Turkey      Turquie   \n",
       "29      uk                  GB            eu  United Kingdom  Royaume-Uni   \n",
       "\n",
       "                   name_de   latitude  longitude  \n",
       "27                Schweden  62.198468  14.896307  \n",
       "28                  Türkei  38.952942  35.439795  \n",
       "29  Vereinigtes Königreich  54.315447  -2.232612  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries.tail(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This file has lots of useful information. It even has the country names is three different languages.\n",
    "\n",
    "Because the data we need is stored in two separate files, we'll want to merge the data somehow. Let's determine which column we can use to join this data. `country` looks like a good option. However, we don't need all of the columns in the `countries` `DataFrame`. To select certain columns, we use the name bracket notation we used to reorder the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "country_names = countries[['country', 'country_group', 'name_en']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>country_group</th>\n",
       "      <th>name_en</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>at</td>\n",
       "      <td>eu</td>\n",
       "      <td>Austria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>be</td>\n",
       "      <td>eu</td>\n",
       "      <td>Belgium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country country_group  name_en\n",
       "0      at            eu  Austria\n",
       "1      be            eu  Belgium"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_names.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`pandas` includes an easy-to-use merge function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "unemployment = pd.merge(unemployment, country_names, on='country')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Merging is often more complex than this example. If you want to merge on multiple columns, you can pass a list of column names to the `on` parameter.\n",
    "\n",
    "```\n",
    "pd.merge(first, second, on=['name', 'id'])\n",
    "```\n",
    "\n",
    "You might even need to merge on columns with different names. To do so, use the `left_on` and `right_on` parameters, where the first listed `DataFrame` is the \"left\" one and the second is the \"right.\" It might look something this.\n",
    "\n",
    "```\n",
    "pd.merge(one, two, left_on='city', right_on='city_name')\n",
    "```\n",
    "\n",
    "For more information on merging, check the [documentation](http://pandas.pydata.org/pandas-docs/stable/merging.html#database-style-dataframe-joining-merging).\n",
    "\n",
    "`pandas` also provides a `.merge()` method that can act on a `DataFrame`. You can read more about that [here](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.merge.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>seasonality</th>\n",
       "      <th>year_month</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>unemployment</th>\n",
       "      <th>unemployment_rate</th>\n",
       "      <th>country_group</th>\n",
       "      <th>name_en</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>at</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1993.01</td>\n",
       "      <td>1993</td>\n",
       "      <td>1</td>\n",
       "      <td>171000</td>\n",
       "      <td>4.5</td>\n",
       "      <td>eu</td>\n",
       "      <td>Austria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>at</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1993.02</td>\n",
       "      <td>1993</td>\n",
       "      <td>2</td>\n",
       "      <td>175000</td>\n",
       "      <td>4.6</td>\n",
       "      <td>eu</td>\n",
       "      <td>Austria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>at</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1993.03</td>\n",
       "      <td>1993</td>\n",
       "      <td>3</td>\n",
       "      <td>166000</td>\n",
       "      <td>4.4</td>\n",
       "      <td>eu</td>\n",
       "      <td>Austria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>at</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1993.04</td>\n",
       "      <td>1993</td>\n",
       "      <td>4</td>\n",
       "      <td>157000</td>\n",
       "      <td>4.1</td>\n",
       "      <td>eu</td>\n",
       "      <td>Austria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>at</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1993.05</td>\n",
       "      <td>1993</td>\n",
       "      <td>5</td>\n",
       "      <td>147000</td>\n",
       "      <td>3.9</td>\n",
       "      <td>eu</td>\n",
       "      <td>Austria</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country seasonality  year_month  year  month  unemployment  \\\n",
       "0      at         nsa     1993.01  1993      1        171000   \n",
       "1      at         nsa     1993.02  1993      2        175000   \n",
       "2      at         nsa     1993.03  1993      3        166000   \n",
       "3      at         nsa     1993.04  1993      4        157000   \n",
       "4      at         nsa     1993.05  1993      5        147000   \n",
       "\n",
       "   unemployment_rate country_group  name_en  \n",
       "0                4.5            eu  Austria  \n",
       "1                4.6            eu  Austria  \n",
       "2                4.4            eu  Austria  \n",
       "3                4.1            eu  Austria  \n",
       "4                3.9            eu  Austria  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unemployment.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's better. We now know that the abbreviation \"at\" corresponds to Austria. We might be curious to check what countries we have data for. The `Series` object includes a `.unique()` method. We'll use this to check the countries. We can select the name either using bracket or dot notation. (While we suggested using brackets above, it *is* sometimes easier to use dot notation. Just be careful.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Austria', 'Belgium', 'Bulgaria', 'Cyprus', 'Czech Republic',\n",
       "       'Germany (including  former GDR from 1991)', 'Denmark', 'Estonia',\n",
       "       'Spain', 'Finland', 'France', 'Greece', 'Croatia', 'Hungary',\n",
       "       'Ireland', 'Italy', 'Lithuania', 'Luxembourg', 'Latvia', 'Malta',\n",
       "       'Netherlands', 'Norway', 'Poland', 'Portugal', 'Romania', 'Sweden',\n",
       "       'Slovenia', 'Slovakia', 'Turkey', 'United Kingdom'], dtype=object)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unemployment.name_en.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get a count of the number of unique countries, wrap the above code with `len()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(unemployment.name_en.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It might be more interesting to know how many observations we actually have. `pandas` has a `Series` method called `.value_counts()` that returns the counts for the unique values in the `Series`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Portugal                                     1008\n",
       "Belgium                                      1008\n",
       "Netherlands                                  1008\n",
       "Sweden                                       1008\n",
       "Spain                                        1008\n",
       "Denmark                                      1008\n",
       "France                                       1008\n",
       "Luxembourg                                   1008\n",
       "Ireland                                      1008\n",
       "United Kingdom                               1002\n",
       "Italy                                         924\n",
       "Finland                                       828\n",
       "Norway                                        786\n",
       "Austria                                       648\n",
       "Poland                                        576\n",
       "Malta                                         576\n",
       "Hungary                                       576\n",
       "Slovakia                                      576\n",
       "Bulgaria                                      576\n",
       "Slovenia                                      576\n",
       "Germany (including  former GDR from 1991)     504\n",
       "Czech Republic                                468\n",
       "Latvia                                        459\n",
       "Lithuania                                     459\n",
       "Greece                                        450\n",
       "Romania                                       423\n",
       "Cyprus                                        396\n",
       "Estonia                                       387\n",
       "Croatia                                       324\n",
       "Turkey                                        210\n",
       "Name: name_en, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unemployment['name_en'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, the `Series` is sorted by values. If you'd like it sorted by index%mdash;country name in this case&mdash;append the `.sort_index()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Austria                                       648\n",
       "Belgium                                      1008\n",
       "Bulgaria                                      576\n",
       "Croatia                                       324\n",
       "Cyprus                                        396\n",
       "Czech Republic                                468\n",
       "Denmark                                      1008\n",
       "Estonia                                       387\n",
       "Finland                                       828\n",
       "France                                       1008\n",
       "Germany (including  former GDR from 1991)     504\n",
       "Greece                                        450\n",
       "Hungary                                       576\n",
       "Ireland                                      1008\n",
       "Italy                                         924\n",
       "Latvia                                        459\n",
       "Lithuania                                     459\n",
       "Luxembourg                                   1008\n",
       "Malta                                         576\n",
       "Netherlands                                  1008\n",
       "Norway                                        786\n",
       "Poland                                        576\n",
       "Portugal                                     1008\n",
       "Romania                                       423\n",
       "Slovakia                                      576\n",
       "Slovenia                                      576\n",
       "Spain                                        1008\n",
       "Sweden                                       1008\n",
       "Turkey                                        210\n",
       "United Kingdom                               1002\n",
       "Name: name_en, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unemployment['name_en'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This will be useful for our analysis. The maximum number of observations for a given country for this time period is 1,008 observations. We'll note that certain countries, such as Turkey, have far less data.\n",
    "\n",
    "How about finding the date range for this data set? Let's look at the minimum and maximum years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1983, 2010)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unemployment['year'].min(), unemployment['year'].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we should pause for a moment and think about what data we really care about. For our purposes, the variable of interest is `unemployment_rate`. The number of observations by country only reflect the number of instances for each country name. It is possible, maybe even expected, to have some missing data. Let's find out how many unemployment rate values are missing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "945"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unemployment['unemployment_rate'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `.isnull()` method returns a corresponding boolean value for each entry in the unemployment rate `Series`. In Python `True` is equivalent to 1 and `False` is equivalent to 0. Thus, when we add the result (with `.sum()`), we get a count for the *total* number of missing values.\n",
    "\n",
    "What if we'd like to know how many missing values exist at the *country* level? We can take the main part of what we had above and create a new column in the `DataFrame`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "unemployment['unemployment_rate_null'] = unemployment['unemployment_rate'].isnull()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To count the number of missing values for each country, we introduce the `.groupby()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name_en\n",
       "Austria                                        0.0\n",
       "Belgium                                        0.0\n",
       "Bulgaria                                     180.0\n",
       "Croatia                                      216.0\n",
       "Cyprus                                         0.0\n",
       "Czech Republic                                 0.0\n",
       "Denmark                                        0.0\n",
       "Estonia                                        0.0\n",
       "Finland                                        0.0\n",
       "France                                         0.0\n",
       "Germany (including  former GDR from 1991)      0.0\n",
       "Greece                                         0.0\n",
       "Hungary                                       36.0\n",
       "Ireland                                        0.0\n",
       "Italy                                          0.0\n",
       "Latvia                                         0.0\n",
       "Lithuania                                      0.0\n",
       "Luxembourg                                     0.0\n",
       "Malta                                        180.0\n",
       "Netherlands                                    0.0\n",
       "Norway                                         0.0\n",
       "Poland                                        72.0\n",
       "Portugal                                       0.0\n",
       "Romania                                        0.0\n",
       "Slovakia                                     108.0\n",
       "Slovenia                                      36.0\n",
       "Spain                                        117.0\n",
       "Sweden                                         0.0\n",
       "Turkey                                         0.0\n",
       "United Kingdom                                 0.0\n",
       "Name: unemployment_rate_null, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unemployment.groupby('name_en')['unemployment_rate_null'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's explain what just happened. We start with our `DataFrame`. We tell `pandas` that we want to group the data by country name&mdash;that's what goes in the parentheses. Next, we need to tell it what column we'd like to perform the `.sum()` operation on. In this case, it's the indicator for whether or not the unemployment rate was missing.\n",
    "\n",
    "As we saw above, the number of records for each country differs. We might, then, want to have the missing values by country shown as percentages. Let's create a new `DataFrame` for this.\n",
    "\n",
    "We'll take the code from above and set the `as_index` parameter to `False`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "unemployment_rate = unemployment.groupby('name_en', as_index=False)['unemployment_rate_null'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`unemployment_rate` is a `DataFrame` with the information from cell 34, above. It's important to note that using `as_index=False` in `.groupby()` only works if the grouping column(s) are not the same as the columns on which we're performing the operation.\n",
    "\n",
    "Also, to group by several columns, simply pass in a list of column names to `.groupby()`.\n",
    "\n",
    "```\n",
    "unemployment.groupby(['name_en', 'seasonality'])['unemployment_rate'].mean()\n",
    "```\n",
    "\n",
    "Now, let's add the number of observations by country to the `DataFrame`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "unemployment_rate['n_obs'] = unemployment.groupby('name_en')['name_en'].count().values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we need to use the `values` attribute to get an array of the counts. Excluding `values` will result in a column full of `NaN`s. This is because the index in `unemployment.groupby('name_en')['name_en'].count()` is a list of the country names. When creating a new column, `pandas` tries to match on index. Recall that the default index values for a `DataFrame` is a sequence of integers.\n",
    "\n",
    "Because we know (or have noticed) that the `.groupby()` function returns the values in alphabetical order, we can simply set the new column to the list of values, as we have done. You can, however, be more explicit and create another `DataFrame` and merge on country name.\n",
    "\n",
    "Finally, let's create the column for the percentage of missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "unemployment_rate['null_percentage'] = unemployment_rate['unemployment_rate_null'] / unemployment_rate['n_obs']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name_en</th>\n",
       "      <th>unemployment_rate_null</th>\n",
       "      <th>n_obs</th>\n",
       "      <th>null_percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Austria</td>\n",
       "      <td>0.0</td>\n",
       "      <td>648</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1008</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>180.0</td>\n",
       "      <td>576</td>\n",
       "      <td>0.312500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Croatia</td>\n",
       "      <td>216.0</td>\n",
       "      <td>324</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cyprus</td>\n",
       "      <td>0.0</td>\n",
       "      <td>396</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>0.0</td>\n",
       "      <td>468</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1008</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Estonia</td>\n",
       "      <td>0.0</td>\n",
       "      <td>387</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Finland</td>\n",
       "      <td>0.0</td>\n",
       "      <td>828</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>France</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1008</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Germany (including  former GDR from 1991)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>504</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Greece</td>\n",
       "      <td>0.0</td>\n",
       "      <td>450</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Hungary</td>\n",
       "      <td>36.0</td>\n",
       "      <td>576</td>\n",
       "      <td>0.062500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Ireland</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1008</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Italy</td>\n",
       "      <td>0.0</td>\n",
       "      <td>924</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Latvia</td>\n",
       "      <td>0.0</td>\n",
       "      <td>459</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Lithuania</td>\n",
       "      <td>0.0</td>\n",
       "      <td>459</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1008</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Malta</td>\n",
       "      <td>180.0</td>\n",
       "      <td>576</td>\n",
       "      <td>0.312500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1008</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Norway</td>\n",
       "      <td>0.0</td>\n",
       "      <td>786</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Poland</td>\n",
       "      <td>72.0</td>\n",
       "      <td>576</td>\n",
       "      <td>0.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1008</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Romania</td>\n",
       "      <td>0.0</td>\n",
       "      <td>423</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Slovakia</td>\n",
       "      <td>108.0</td>\n",
       "      <td>576</td>\n",
       "      <td>0.187500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Slovenia</td>\n",
       "      <td>36.0</td>\n",
       "      <td>576</td>\n",
       "      <td>0.062500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Spain</td>\n",
       "      <td>117.0</td>\n",
       "      <td>1008</td>\n",
       "      <td>0.116071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1008</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>0.0</td>\n",
       "      <td>210</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1002</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      name_en  unemployment_rate_null  n_obs  \\\n",
       "0                                     Austria                     0.0    648   \n",
       "1                                     Belgium                     0.0   1008   \n",
       "2                                    Bulgaria                   180.0    576   \n",
       "3                                     Croatia                   216.0    324   \n",
       "4                                      Cyprus                     0.0    396   \n",
       "5                              Czech Republic                     0.0    468   \n",
       "6                                     Denmark                     0.0   1008   \n",
       "7                                     Estonia                     0.0    387   \n",
       "8                                     Finland                     0.0    828   \n",
       "9                                      France                     0.0   1008   \n",
       "10  Germany (including  former GDR from 1991)                     0.0    504   \n",
       "11                                     Greece                     0.0    450   \n",
       "12                                    Hungary                    36.0    576   \n",
       "13                                    Ireland                     0.0   1008   \n",
       "14                                      Italy                     0.0    924   \n",
       "15                                     Latvia                     0.0    459   \n",
       "16                                  Lithuania                     0.0    459   \n",
       "17                                 Luxembourg                     0.0   1008   \n",
       "18                                      Malta                   180.0    576   \n",
       "19                                Netherlands                     0.0   1008   \n",
       "20                                     Norway                     0.0    786   \n",
       "21                                     Poland                    72.0    576   \n",
       "22                                   Portugal                     0.0   1008   \n",
       "23                                    Romania                     0.0    423   \n",
       "24                                   Slovakia                   108.0    576   \n",
       "25                                   Slovenia                    36.0    576   \n",
       "26                                      Spain                   117.0   1008   \n",
       "27                                     Sweden                     0.0   1008   \n",
       "28                                     Turkey                     0.0    210   \n",
       "29                             United Kingdom                     0.0   1002   \n",
       "\n",
       "    null_percentage  \n",
       "0          0.000000  \n",
       "1          0.000000  \n",
       "2          0.312500  \n",
       "3          0.666667  \n",
       "4          0.000000  \n",
       "5          0.000000  \n",
       "6          0.000000  \n",
       "7          0.000000  \n",
       "8          0.000000  \n",
       "9          0.000000  \n",
       "10         0.000000  \n",
       "11         0.000000  \n",
       "12         0.062500  \n",
       "13         0.000000  \n",
       "14         0.000000  \n",
       "15         0.000000  \n",
       "16         0.000000  \n",
       "17         0.000000  \n",
       "18         0.312500  \n",
       "19         0.000000  \n",
       "20         0.000000  \n",
       "21         0.125000  \n",
       "22         0.000000  \n",
       "23         0.000000  \n",
       "24         0.187500  \n",
       "25         0.062500  \n",
       "26         0.116071  \n",
       "27         0.000000  \n",
       "28         0.000000  \n",
       "29         0.000000  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unemployment_rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the first time we've called a `DataFrame` without something like `.head()`. By default, `pandas` prints 60 rows. In this case, because there are only 30 countries, we see the entire `DataFrame`.\n",
    "\n",
    "As we can see, Croatia has lots of missing data. This `DataFrame` contains useful information&mdash;things to consider&mdash;when analyzing the data.\n",
    "\n",
    "Suppose we wanted to save this as a .csv file. For this, we'd use the `.to_csv()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "unemployment_rate.to_csv('data/unemployment_missing.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, this method writes the indices. We probably don't want that. Let's edit the code. Let's also be explicit about the type of delimiter we're interested in. (Values can be separated by pipes (`|`), semicolons (`;`), tabs (`\\t`), etc.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "unemployment_rate.to_csv('data/unemployment_missing.csv', index=False, sep=',')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Much better!\n",
    "\n",
    "Let's return to our main `DataFrame`. Now that we have the missing values information in `unemployment_rate`, we can drop the last column we added to `unemployment`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "unemployment.drop('unemployment_rate_null', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's important to specify the `axis` parameter. `axis=1` refers to columns. (`axis=0` refers to rows.) `inplace=True` simply modifies the actual `DataFrame`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>seasonality</th>\n",
       "      <th>year_month</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>unemployment</th>\n",
       "      <th>unemployment_rate</th>\n",
       "      <th>country_group</th>\n",
       "      <th>name_en</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>at</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1993.01</td>\n",
       "      <td>1993</td>\n",
       "      <td>1</td>\n",
       "      <td>171000</td>\n",
       "      <td>4.5</td>\n",
       "      <td>eu</td>\n",
       "      <td>Austria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>at</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1993.02</td>\n",
       "      <td>1993</td>\n",
       "      <td>2</td>\n",
       "      <td>175000</td>\n",
       "      <td>4.6</td>\n",
       "      <td>eu</td>\n",
       "      <td>Austria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>at</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1993.03</td>\n",
       "      <td>1993</td>\n",
       "      <td>3</td>\n",
       "      <td>166000</td>\n",
       "      <td>4.4</td>\n",
       "      <td>eu</td>\n",
       "      <td>Austria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>at</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1993.04</td>\n",
       "      <td>1993</td>\n",
       "      <td>4</td>\n",
       "      <td>157000</td>\n",
       "      <td>4.1</td>\n",
       "      <td>eu</td>\n",
       "      <td>Austria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>at</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1993.05</td>\n",
       "      <td>1993</td>\n",
       "      <td>5</td>\n",
       "      <td>147000</td>\n",
       "      <td>3.9</td>\n",
       "      <td>eu</td>\n",
       "      <td>Austria</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country seasonality  year_month  year  month  unemployment  \\\n",
       "0      at         nsa     1993.01  1993      1        171000   \n",
       "1      at         nsa     1993.02  1993      2        175000   \n",
       "2      at         nsa     1993.03  1993      3        166000   \n",
       "3      at         nsa     1993.04  1993      4        157000   \n",
       "4      at         nsa     1993.05  1993      5        147000   \n",
       "\n",
       "   unemployment_rate country_group  name_en  \n",
       "0                4.5            eu  Austria  \n",
       "1                4.6            eu  Austria  \n",
       "2                4.4            eu  Austria  \n",
       "3                4.1            eu  Austria  \n",
       "4                3.9            eu  Austria  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unemployment.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we know about the missing values, we have to deal with them. There are two main options:\n",
    "\n",
    "* fill the missing values with some other values\n",
    "* do not use observations with missing values\n",
    "    * depending on the analysis, we may want to exclude entire countries\n",
    "    \n",
    "Because countries with missing unemployment rate data have at least 36 missing values, which is too many to fill, we'll take the second approach and exclude missing values from our primary analyses.\n",
    "\n",
    "Instead of just getting rid of that data, it might make sense to store it in a separate `DataFrame`. This way, we could answer questions such as, \"do missing values occur during certain months (or years) more frequently?\" With this, we will introduce the concept of *boolean indexing* for filtering data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "unemployment_rate_missing = unemployment[unemployment['unemployment_rate'].isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recall that `unemployment['unemployment_rate'].isnull()` produces an array of boolean values. We used this previously when counting the number of missing values, though we did not see its output. Let's see some of that now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3    False\n",
       "4    False\n",
       "5    False\n",
       "6    False\n",
       "7    False\n",
       "8    False\n",
       "9    False\n",
       "Name: unemployment_rate, dtype: bool"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unemployment['unemployment_rate'].isnull()[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To create `unemployment_rate_missing`, we're indexing `unemployment` with the array above. This returns only the rows where the value in the array is `True`. Let's see if it worked."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>seasonality</th>\n",
       "      <th>year_month</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>unemployment</th>\n",
       "      <th>unemployment_rate</th>\n",
       "      <th>country_group</th>\n",
       "      <th>name_en</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1656</th>\n",
       "      <td>bg</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1995.01</td>\n",
       "      <td>1995</td>\n",
       "      <td>1</td>\n",
       "      <td>391000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>eu</td>\n",
       "      <td>Bulgaria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1657</th>\n",
       "      <td>bg</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1995.02</td>\n",
       "      <td>1995</td>\n",
       "      <td>2</td>\n",
       "      <td>387000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>eu</td>\n",
       "      <td>Bulgaria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1658</th>\n",
       "      <td>bg</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1995.03</td>\n",
       "      <td>1995</td>\n",
       "      <td>3</td>\n",
       "      <td>378000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>eu</td>\n",
       "      <td>Bulgaria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1659</th>\n",
       "      <td>bg</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1995.04</td>\n",
       "      <td>1995</td>\n",
       "      <td>4</td>\n",
       "      <td>365000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>eu</td>\n",
       "      <td>Bulgaria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1660</th>\n",
       "      <td>bg</td>\n",
       "      <td>nsa</td>\n",
       "      <td>1995.05</td>\n",
       "      <td>1995</td>\n",
       "      <td>5</td>\n",
       "      <td>346000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>eu</td>\n",
       "      <td>Bulgaria</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     country seasonality  year_month  year  month  unemployment  \\\n",
       "1656      bg         nsa     1995.01  1995      1        391000   \n",
       "1657      bg         nsa     1995.02  1995      2        387000   \n",
       "1658      bg         nsa     1995.03  1995      3        378000   \n",
       "1659      bg         nsa     1995.04  1995      4        365000   \n",
       "1660      bg         nsa     1995.05  1995      5        346000   \n",
       "\n",
       "      unemployment_rate country_group   name_en  \n",
       "1656                NaN            eu  Bulgaria  \n",
       "1657                NaN            eu  Bulgaria  \n",
       "1658                NaN            eu  Bulgaria  \n",
       "1659                NaN            eu  Bulgaria  \n",
       "1660                NaN            eu  Bulgaria  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unemployment_rate_missing.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is also possible to specify multiple conditions using the `&` operator, but each condition needs to be inside of parentheses. The `.isin()` method, which takes a `list` of values, is useful when you're interested in conditioning on multiple values on a given column. For example, if you want to select multiple countries.\n",
    "\n",
    "Now, we're ready to remove the missing data in `unemployment`. To do this, we can use the `.dropna()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "unemployment.dropna(subset=['unemployment_rate'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this point, you might be curious to know what the highest unemployment rates were. For this, we'll use the `.sort()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>seasonality</th>\n",
       "      <th>year_month</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>unemployment</th>\n",
       "      <th>unemployment_rate</th>\n",
       "      <th>country_group</th>\n",
       "      <th>name_en</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15526</th>\n",
       "      <td>pl</td>\n",
       "      <td>nsa</td>\n",
       "      <td>2004.02</td>\n",
       "      <td>2004</td>\n",
       "      <td>2</td>\n",
       "      <td>3531000</td>\n",
       "      <td>20.9</td>\n",
       "      <td>eu</td>\n",
       "      <td>Poland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15525</th>\n",
       "      <td>pl</td>\n",
       "      <td>nsa</td>\n",
       "      <td>2004.01</td>\n",
       "      <td>2004</td>\n",
       "      <td>1</td>\n",
       "      <td>3520000</td>\n",
       "      <td>20.7</td>\n",
       "      <td>eu</td>\n",
       "      <td>Poland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15514</th>\n",
       "      <td>pl</td>\n",
       "      <td>nsa</td>\n",
       "      <td>2003.02</td>\n",
       "      <td>2003</td>\n",
       "      <td>2</td>\n",
       "      <td>3460000</td>\n",
       "      <td>20.7</td>\n",
       "      <td>eu</td>\n",
       "      <td>Poland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5663</th>\n",
       "      <td>es</td>\n",
       "      <td>sa</td>\n",
       "      <td>2010.09</td>\n",
       "      <td>2010</td>\n",
       "      <td>9</td>\n",
       "      <td>4773000</td>\n",
       "      <td>20.6</td>\n",
       "      <td>eu</td>\n",
       "      <td>Spain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15527</th>\n",
       "      <td>pl</td>\n",
       "      <td>nsa</td>\n",
       "      <td>2004.03</td>\n",
       "      <td>2004</td>\n",
       "      <td>3</td>\n",
       "      <td>3475000</td>\n",
       "      <td>20.6</td>\n",
       "      <td>eu</td>\n",
       "      <td>Poland</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      country seasonality  year_month  year  month  unemployment  \\\n",
       "15526      pl         nsa     2004.02  2004      2       3531000   \n",
       "15525      pl         nsa     2004.01  2004      1       3520000   \n",
       "15514      pl         nsa     2003.02  2003      2       3460000   \n",
       "5663       es          sa     2010.09  2010      9       4773000   \n",
       "15527      pl         nsa     2004.03  2004      3       3475000   \n",
       "\n",
       "       unemployment_rate country_group name_en  \n",
       "15526               20.9            eu  Poland  \n",
       "15525               20.7            eu  Poland  \n",
       "15514               20.7            eu  Poland  \n",
       "5663                20.6            eu   Spain  \n",
       "15527               20.6            eu  Poland  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unemployment.sort_values(by='unemployment_rate', ascending=False)[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above code creates a copy of the `DataFrame`, sorts it in *descending* order, and prints the first five rows.\n",
    "\n",
    "You may have noticed that the data set includes a `seasonality` column, which we haven't yet discussed. The unemployment rate in this data is actually calculated in three separate ways. Let's look at the values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['nsa', 'sa', 'trend'], dtype=object)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unemployment['seasonality'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The three options above correspond to:\n",
    "\n",
    "* not seasonally adjusted\n",
    "* seasonally adjusted\n",
    "* trend cycle\n",
    "\n",
    "We'll stick with seasonally adjusted data so that the values are more comparable. Let's look at the highest unemployment rates in this context."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>seasonality</th>\n",
       "      <th>year_month</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>unemployment</th>\n",
       "      <th>unemployment_rate</th>\n",
       "      <th>country_group</th>\n",
       "      <th>name_en</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5664</th>\n",
       "      <td>es</td>\n",
       "      <td>sa</td>\n",
       "      <td>2010.10</td>\n",
       "      <td>2010</td>\n",
       "      <td>10</td>\n",
       "      <td>4758000</td>\n",
       "      <td>20.6</td>\n",
       "      <td>eu</td>\n",
       "      <td>Spain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5663</th>\n",
       "      <td>es</td>\n",
       "      <td>sa</td>\n",
       "      <td>2010.09</td>\n",
       "      <td>2010</td>\n",
       "      <td>9</td>\n",
       "      <td>4773000</td>\n",
       "      <td>20.6</td>\n",
       "      <td>eu</td>\n",
       "      <td>Spain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5662</th>\n",
       "      <td>es</td>\n",
       "      <td>sa</td>\n",
       "      <td>2010.08</td>\n",
       "      <td>2010</td>\n",
       "      <td>8</td>\n",
       "      <td>4739000</td>\n",
       "      <td>20.5</td>\n",
       "      <td>eu</td>\n",
       "      <td>Spain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5665</th>\n",
       "      <td>es</td>\n",
       "      <td>sa</td>\n",
       "      <td>2010.11</td>\n",
       "      <td>2010</td>\n",
       "      <td>11</td>\n",
       "      <td>4723000</td>\n",
       "      <td>20.4</td>\n",
       "      <td>eu</td>\n",
       "      <td>Spain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15702</th>\n",
       "      <td>pl</td>\n",
       "      <td>sa</td>\n",
       "      <td>2002.10</td>\n",
       "      <td>2002</td>\n",
       "      <td>10</td>\n",
       "      <td>3471000</td>\n",
       "      <td>20.3</td>\n",
       "      <td>eu</td>\n",
       "      <td>Poland</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      country seasonality  year_month  year  month  unemployment  \\\n",
       "5664       es          sa     2010.10  2010     10       4758000   \n",
       "5663       es          sa     2010.09  2010      9       4773000   \n",
       "5662       es          sa     2010.08  2010      8       4739000   \n",
       "5665       es          sa     2010.11  2010     11       4723000   \n",
       "15702      pl          sa     2002.10  2002     10       3471000   \n",
       "\n",
       "       unemployment_rate country_group name_en  \n",
       "5664                20.6            eu   Spain  \n",
       "5663                20.6            eu   Spain  \n",
       "5662                20.5            eu   Spain  \n",
       "5665                20.4            eu   Spain  \n",
       "15702               20.3            eu  Poland  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unemployment[unemployment['seasonality'] == 'sa'].sort_values(by='unemployment_rate', ascending=False)[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Spain has the highest seasonally adjusted unemployment rate.\n",
    "\n",
    "The best way to get a sense of this data is to plot it. Next, we'll start to look at some basic plotting with `pandas`. Before we begin, let's sort the data by country and date. This is good practice and is especially important when using `pandas`'s `.plot()` method because the x-axis values are based on the indices. When we sort, the index values remain unchanged. Thus, we need to reset them. The `drop` parameter tells `pandas` to construct a `DataFrame` *without* adding a column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "unemployment.sort_values(by=['name_en', 'year_month'], inplace=True)\n",
    "unemployment.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at Spain's unemployment rate (only because it was the highest) across time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "spain = unemployment[(unemployment['name_en'] == 'Spain') &\n",
    "                     (unemployment['seasonality'] == 'sa')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fb2fc3da20>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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J7OZzb5c2UUtjfW6/vCNJcmR4PD/uGZiUGgEAAICyddtOdrq4a3lHFSupPuEN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\n01cxOjoD1+0wkaRwLKGB8eiMt387O6ojl0KSJI/Do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B448/jkWLFgEAzp07h66uLos6zK5zUCK3IxHWhAWABQsWwN3d3aLOwMAATp8+DQAI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\nERERERERERERERGpyP8BSgeVWzwo1/oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fb2fc5e240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "spain['unemployment_rate'].plot(figsize=(10, 8), color='#348ABD')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the values along the x-axis represent the indices associated with Spain in the sorted `unemployment` `DataFrame`. Wouldn't it be nice if, instead, we could show the time period associated with the various unemployment rates for Spain? It might also be interesting to compare Spain's unemployment rate with its neighbor to the west, Portugal.\n",
    "\n",
    "Let's first create a `DataFrame` that contains the unemployment data for both countries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ps = unemployment[(unemployment['name_en'].isin(['Portugal', 'Spain'])) &\n",
    "                  (unemployment['seasonality'] == 'sa')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we'll add a `datetime` object. `pandas` provides a `to_datetime()` function that makes this relatively simple. It converts an argument&mdash;a single value or an array of values&mdash;to `datetime`. (Note that the return value [depends on the input](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.to_datetime.html).) If we were interested in March 23, 1868, for example, we could do the following."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('1868-03-23 00:00:00')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime('1868/3/23')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The argument doesn't necessarily have to be specified in the `yyyy/mm/dd` format. You could list it as `mm/dd/yyyy`, but it's a good idea to be explicit. As a result, we pass in a valid string format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('1868-03-23 00:00:00')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime('3/23/1868', format='%m/%d/%Y')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create the `datetime` object and add it to the `DataFrame` as a column named `date`. For this, we'll use the `.insert()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ps.insert(loc=0, column='date',\n",
    "          value=pd.to_datetime(ps['year'].astype(str) + '/' + ps['month'].astype(str) + '/1'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's only keep certain columns, rename them, and reshape the `DataFrame`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>Portugal</th>\n",
       "      <th>Spain</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time Period</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-08-01</th>\n",
       "      <td>11.1</td>\n",
       "      <td>20.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-09-01</th>\n",
       "      <td>11.1</td>\n",
       "      <td>20.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-10-01</th>\n",
       "      <td>11.0</td>\n",
       "      <td>20.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-11-01</th>\n",
       "      <td>10.9</td>\n",
       "      <td>20.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-12-01</th>\n",
       "      <td>10.9</td>\n",
       "      <td>20.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country      Portugal  Spain\n",
       "Time Period                 \n",
       "2010-08-01       11.1   20.5\n",
       "2010-09-01       11.1   20.6\n",
       "2010-10-01       11.0   20.6\n",
       "2010-11-01       10.9   20.4\n",
       "2010-12-01       10.9   20.2"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ps = ps[['date', 'name_en', 'unemployment_rate']]\n",
    "ps.columns = ['Time Period', 'Country', 'Unemployment Rate']\n",
    "ps = ps.pivot(index='Time Period', columns='Country', values='Unemployment Rate')\n",
    "ps.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice the indices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fb323d0a90>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VtU+ENwAAAPC5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\na32MV0HvqFf+amC7ef5LulgJsCYq2+64ZGfqH7pXCLBS6qyZqb/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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fb310dd2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ps.plot(figsize=(10, 8), title='Unemployment Rate\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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